A set of genes previously implicated in the hypoxia
response might be an important modulator in the rat ear
tissue response to mechanical stretch
The MIT Faculty has made this article openly available. Please share
how this access benefits you. Your story matters.
Citation
BMC Genomics. 2007 Nov 23;8(1):430
As Published
http://dx.doi.org/10.1186/1471-2164-8-430
Publisher
BioMed Central Ltd
Version
Final published version
Accessed
Thu May 26 19:16:15 EDT 2016
Citable Link
http://hdl.handle.net/1721.1/58998
Terms of Use
Creative Commons Attribution
Detailed Terms
http://creativecommons.org/licenses/by/2.0
BMC Genomics
BioMed Central
Open Access
Methodology article
A set of genes previously implicated in the hypoxia response might
be an important modulator in the rat ear tissue response to
mechanical stretch
Vishal Saxena*1,2, Dennis Orgill1,2 and Isaac Kohane2,3,4
Address: 1Surgery, Brigham and Women's Hospital, Boston, MA, USA, 2Harvard Medical School, Boston, MA, USA, 3Children's Hospital
Informatics Program, Children's Hospital, Boston, MA, USA and 4HST, Massachusetts Institute of Technology, Cambridge, MA, USA
Email: Vishal Saxena* - vishal@mit.edu; Dennis Orgill - dorgill@partners.org; Isaac Kohane - isaac_kohane@harvard.edu
* Corresponding author
Published: 23 November 2007
BMC Genomics 2007, 8:430
doi:10.1186/1471-2164-8-430
Received: 5 November 2006
Accepted: 23 November 2007
This article is available from: http://www.biomedcentral.com/1471-2164/8/430
© 2007 Saxena et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: Wounds are increasingly important in our aging societies. Pathologies such as
diabetes predispose patients to chronic wounds that can cause pain, infection, and amputation. The
vacuum assisted closure device shows remarkable outcomes in wound healing. Its mechanism of
action is unclear despite several hypotheses advanced. We previously hypothesized that
micromechanical forces can heal wounds. To understand better the biological response of soft
tissue to forces, rat ears in vivo were stretched and their gene expression patterns over time
obtained. The absolute enrichment (AE) algorithm that obtains a combined up and down regulated
picture of the expression analysis was implemented.
Results: With the use of AE, the hypoxia gene set was the most important at a highly significant
level. A co-expression network analysis showed that important co-regulated members of the
hypoxia pathway include a glucose transporter (slc2a8), heme oxygenase, and nitric oxide
synthase2 among others.
Conclusion: It appears that the hypoxia pathway may be an important modulator of response of
soft tissue to forces. This finding gives us insights not only into the underlying biology, but also into
clinical interventions that could be designed to mimic within wounded tissue the effects of forces
without all the negative effects that forces themselves create.
Background
Clinical context
Worldwide, wounds pose a major health issue. Lower
extremity ulcers alone cost the US Medicare system $1.5
billion. Unless wound therapies see a large improvement,
we will see escalating treatment costs and morbidity as the
population ages and as the incidence of diabetes and
obesity increases. An understanding of the mechanisms
underlying wound healing will shed light on how normal
physiology adapts to changes in the normal homeostatic
environment. The vacuum assisted closure (VAC) device
(KCI, San Antonio Texas) is a relatively new modality in
wound healing. Although the device has been shown to
accelerate wound healing, its mode of action remains to
be proven convincingly. Figure 1 shows the application of
the device. This involves packing a polyurethane sponge
into the wound bed and then sealing the wound including
the sponge with an occlusive dressing that has one outlet
Page 1 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
http://www.biomedcentral.com/1471-2164/8/430
able to understand the molecular mechanisms that underlie tissue response to forces, then we could substitute
forces with interventions that mimic these molecular
events. The effects of tissue expansion mechanisms [4] are
little studied in the literature. A rat ear was chosen as the
system in which to study the effects of forces on tissues in
a perfused system (Figure 2). The rat ear is thin and the
blood vessels can be easily visualized under a light microscope. Further, it is highly amenable to force experiments
in vivo.
Results
Figure 1application
Vacuum
Vacuum application. The upper part of the figure has the vacuum off. The sponge is therefore uncollapsed. The lower part
of the figure has the vacuum on. The sponge has collapsed.
The blowup shows how the skin stretches once the vacuum
is applied.
Enrichment analyses
We ran the absolute enrichment, the upregulated enrichment (the standard gene set enrichment analysis or
GSEA), and the downregulated enrichment analyses on
our dataset as explained in the methods section. The
results are presented in Tables 1 through 3 respectively.
The absolute enrichment analysis produced the hypoxia
gene set as the top ranked gene set of the 173 gene sets
tested. Also, 'response to mechanical stimulus' achieves
the second highest rank. The members of the hypoxia
gene set are listed in Table 4 along with their paired t
scores. Positive t scores (shown in blue) indicate overall
upregulation over the 8 time points, while negative scores
tube going to a vacuum (a vacuum of about 115 mmHg is
applied through the tube). Theories about how this device
obtains its efficacy range from a reduction in bacterial
load [1] to a reduction in edema. The blowup in Figure 1
shows that with the application of the vacuum the skin is
pulled into the intra-strut spacings of the sponge thereby
stretching the skin.
In an earlier publication, we have used a numerical model
to show how the imposition of the vacuum forces the tissue to stretch by small amounts (micromechanically) as
the tissue is 'pulled' into the empty space defined by regular holes in the sponge causing a tension to be applied to
the tissue substrate and through it to the cells embedded
within the substrate [2]. Previous work [3] by Donald Ingber, one of the co-authors on our microstreatch paper [2]
has shown that cells stretched in vitro proliferate, whereas
cells that are not stretched are cell cycle arrested. We thus,
in that paper, hypothesized that through a similar mechanism the cells in the wound bed subjected to stretch by the
application of negative suction pressure are also proliferating and thus helping the wound heal faster [2]. If we are
Figure
rat
force2apparatus
rat force apparatus. Strips of latex (shown in orange) are
pulled by rubber bands (not shown) in three directions.
Page 2 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
http://www.biomedcentral.com/1471-2164/8/430
Table 1: Absolute enrichment
Top gene set
EASE GO categories (for some gene sets)
hypoxia
response_to_mechanical_stimulus
c3_U133_probes
P value
RNA binding (MF), ribonucleoprotein complex (CC), cytosolic ribosome (CC),
structural constituent of ribosome (MF), ribosome (CC), small ribosomal
subunit (CC), cytosolic small ribosomal subunit (CC), eukaryotic 48S initiation
complex (CC), eukaryotic 43S preinitiation complex (CC), nucleic acid binding
(MF)
MAP00340_Histidine_metabolism
c27_U133_probes
Intracellular (CC), regulation of translation (BP), nucleic acid binding (MF), RNA
binding (MF), cell cycle (BP), DNA metabolism (BP), response to DNA damage
stimulus (BP), response to endogenous stimulus (BP), DNA repair (BP),
regulation of translational initiation (BP)
MAP00280_Valine_leucine_and_isoleucine_degradation
c23_U133_probes
c6_U133_probes
Oxidoreductase activity (MF), microbody (CC), peroxisome (CC), transporter
activity (MF), ethanol metabolism (BP), ethanol oxidation (BP), catalytic activity
(MF), cytoplasm (CC), carboxylic acid metabolism (BP), organic acid metabolism
(BP)
Epidermal differentiation (BP), Ectoderm development (BP), histogenesis (BP),
intermediate filament cytoskeleton (CC), Intermediate filament (CC),
morphogenesis (BP), organogenesis (BP), structural molecule activity (MF),
development (BP), cytoskeleton (CC)
mitochondr_HG-U133A_probes
MAP00140_C21_Steroid_hormone_metabolism
(shown in red) indicate overall downregulation over the 8
time points.
It should be noted that in gene set enrichment analysis we
typically do not calculate the P values for each gene separately. Rather we perform the permutation test (as we
explain in the methods section) and look for the P values
of the gene set. This is why we have not listed the P values
of the genes in the hypoxia pathway. In the upregulated
enrichment analysis, 'response to mechanical stimulus'
0.0033
0.015
0.018
0.004
0.016
0.0096
0.012
0.014
0.008
0.0027
achieves the top position. Interestingly, the c26 cluster
which is enriched for Reproduction genes from EASE and
which was derived from Mootha's original gene sets has
obtained second rank in the up regulated analysis. There
are a total of 36 c clusters derived by Mootha through the
use of self organizing maps over the GNF mouse expression atlas which itself is derived from expression profiling
of different mouse tissue [5]. There were no gene sets in
the down regulated analysis that had an ambiguous interpretation and thus none had to be analyzed through
Table 2: Up regulated analysis
Gene set
EASE GO categories
P value
response_to_mechanical_stimulus
c26_U133_probes
Sexual reproduction (BP), reproduction (BP), spermatogenesis (BP), male gamete generation (BP),
intracellular (CC), gametogeneis (BP), chaperone activity (MF), M phase (BP), nuclear division (BP),
spindle (CC)
c6_U133_probes
Epidermal differentiation (BP), Ectoderm development (BP), histogenesis (BP), intermediate filament
cytoskeleton (CC), Intermediate filament (CC), morphogenesis (BP), organogenesis (BP), structural
molecule activity (MF), development (BP), cytoskeleton (CC)
c3_U133_probes
RNA binding (MF), ribonucleoprotein complex (CC), cytosolic ribosome (CC), structural constituent
of ribosome (MF), ribosome (CC), small ribosomal subunit (CC), cytosolic small ribosomal subunit
(CC), eukaryotic 48S initiation complex (CC), eukaryotic 43S preinitiation complex (CC), nucleic
acid binding (MF)
Hum_Fb_Serum_EarlyTF
OXPHOS_HG-U133A_probes
c27_U133_probes
Intracellular (CC), regulation of translation (BP), nucleic acid binding (MF), RNA binding (MF), cell
cycle (BP), DNA metabolism (BP), response to DNA damage stimulus (BP), response to endogenous
stimulus (BP), DNA repair (BP), regulation of translational initiation (BP)
mitochondr_HG-U133A_probes
MAP00710_Carbon_fixation
c14_U133_probes
0.085
0.014
0.023
0.003
0
0.131
0
0.018
0.004
0.002
Page 3 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
http://www.biomedcentral.com/1471-2164/8/430
Table 3: Down regulated analysis
Gene set
Table 5: Co-expression network analysis
P value
MAP00280_Valine_leucine_and_isoleucine_degradation
MAP00340_Histidine_metabolism
MAP00310_Lysine_degradation
MAP00380_Tryptophan_metabolism
MAP00632_Benzoate_degradation
hypoxia
MAP00562_Inositol_phosphate_metabolism
MAP00052_Galactose_metabolism
MAP00521_Streptomycin_biosynthesis
cluster7_LPS_mouse_urinary.txt
0.008
0.005
0
0.002
0.01
0
0.004
0
0.001
0
EASE. The Hypoxia gene set obtains rank 6 in the down
regulated enrichment analysis. To see how members of
the hypoxia gene set may be co-regulated, we ran a coexpression network analysis.
Co-expression network analysis
Pairwise correlations between the gene expression trajectories provide the correlation network weights shown in
Table 5. The correlation network structure based on a
threshold of 0.5 is shown in Figure 3. To obtain the connectivity values (Table 5), absolute values of the correlations were summed for each gene.
Gene symbol
(or probeset when symbol unavailable)
Nos2
Casp12
Casp4
Brd4
Brd2
Ep300_1369307_at
Casp1
Hmox1
Slc2a8
Sod3
RGD1562168_predicted
Epas1
1371289_at
Ep300_1373916_at
Camk2g
Connectivity values
(in descending order)
5.6852
5.365
5.3197
5.038
4.8786
4.7816
4.7395
4.6694
4.5987
4.5886
4.3523
4.1956
4.1945
3.8843
2.888
t scores) and then splitting them by inspection of their
time trajectories into two groups. These results are presented in Figures 4 through 6.
Expression ratio plot over time
The hypoxia gene set members were further visualized by
plotting the stretch to control expression values in three
groups. The first group was made up of the positively
upregulated members of the hypoxia pathway (positive
given by the sign of the paired t score). The second and
third groups were obtained by taking the remaining members of the hypoxia pathway (these all had negative paired
The time trajectories of the members of the hypoxia gene
set show two prototypical behaviors. They show either a
sharp spike at earlier time points that is then dampened or
they show a sharp dip at earlier time points that then
recovers at later time points. These two are displayed on
the co-expression network graph (Figure 3). We note that
the members shown in light green shading show the first
prototypical behavior while members negatively correlated with this set show the dip behavior (Casp12 for
example). Ep300_1373916_at and RGD_1561628_predicted are negatively correlated with Casp12 and thus show
a rise at earlier time points but we haven't shaded these
because their rise isn't as pronounced.
Table 4: Hypoxia geneset details
Discussion
Hypoxia Probe Set ID
Gene symbol (or title)
1368286_at
1370080_at
1381936_at
1371719_at
1371289_at
1375650_at
1368322_at
1369703_at
1387605_at
1387818_at
1387667_at
1373916_at
1369186_at
1374863_at
Slc2a8
Hmox1
Camk2g
Brd2
unknown
Brd4
Sod3
Epas1
Casp12
Casp4
Nos2
Ep300
Casp1
Similar to retinoid binding
protein 7 (predicted)
Ep300
1369307_at
t score
2.29
2.03
1.48
1.17
-0.44
-0.51
-1.07
-1.19
-1.19
-1.26
-1.52
-1.65
-1.67
-1.73
-2.11
The response to mechanical stimulus' gene set was ranked
at number two in our absolute enrichment analysis and it
was ranked at number one in our upregulated enrichment
analysis. This gives us a strong measure of confidence that
our t paired statistic is capturing relevant themes in our rat
ear stretch system.
In the absolute enrichment framework, Permutation testing [5] gave the hypoxia gene set a highly significant P
value of 0.0033 at the 0.05 level. The two prototypical
behaviors seen in the time trajectories (seen in Figures 4
through 6) tell us that the genes in our hypoxia pathway
show either a sharp rise or a sharp fall at earlier time
points and then recover. This behavior was then compared with what has been reported in the literature. Specifically, we note that SOD3 shows a decline at earlier
time points and then a recovery while NOS2, Slc2a8, and
Page 4 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
http://www.biomedcentral.com/1471-2164/8/430
Figure 3
Co-expression
network
Co-expression network. NOS2, Hmox1, slc2a8, and Epas1 all show a rise in the early time points while Casp12 (and genes
positively correlated with it) show an early drop. The figures underneath these genes show this time trajectory with dashed
lines showing that the later time points do not necessarily show a flat response. The correlations between genes are shown
next to the lines connecting genes.
Hmox1 all show sharp rises at earlier time points. Maiti et
al [6] have stated that "under hypoxic stress, the cellular
defence systems such as antioxidant enzymes (GPx, GR,
SOD, etc.) get disturbed and their activity decreases." Further, they report that in the rat brain, hypoxia leads to an
increase in nitric oxide. In our system, NOS2 is sharply rising at earlier time points while SOD3 is falling at earlier
time points.
It has also been reported in the literature that NO production upregulates heme oxygenase (Hmox-1) production
[7,8]. This may explain why we see a sharp rise in Hmox
in the early time points. It also may explain why on our
co-expression network graph, NOS2 and Hmox show a
positive correlation of 0.69. At later time points, however,
NOS2 and Hmox-1 do not move together (for example
NOS2 shows downregulation while Hmox shows upregulation). Nitric oxide isn't the only mediator of Hmox-1
upregulation. For example, it has been reported that
Hmox-1 is the major stress protein induced by UVA,
hydrogen peroxide and arsenite [9]. Further, it is known
that Hmox-1 expression is linked to tissue stretch. Many
pathways lead to heme oxygenase I expression through
renal injury [7]. Mechanical stress has been shown to
cause oxidant stress [10] and Hmox-1 levels are increased
when cells are exposed to oxidative stress [11]. Hmox-1
prevents oxidant-induced microvascular leukocyte adhesion [12,13]. Hmox-1 has cytoprotective roles [14], and is
anti-inflammatory [15].
Hmox-1 itself is a negative regulator of NO [16,17]. Thus,
if Hmox-1 is upregulated independently of NO (as may be
Page 5 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
Figure
The
paired
trajectories
t 4score of the genes that showed an overall positive
The trajectories of the genes that showed an overall positive
paired t score. Not all time points however are greater than
one (when we compare genes by taking differences, upregulated genes are positive, while when we compare genes by
taking ratios, upregulated genes show expression ratios
greater than one) as we see for example with Camk2g.
happening at the later time points), then NO is strongly
inhibited. For example, hemin can upregulate Hmox-1
(independently of NO). This induction has been shown
to strongly inhibit NO production of LPS-activated macrophages [18,19]. Loike et al.[20] have reported that
hypoxia leads to glucose transporter expression in
endothelial cells. Thus, it may be that we see a sharp spike
in slc2a8 (which is a glucose transporter [21,22]) as a consequence of hypoxia at earlier time points in our system.
http://www.biomedcentral.com/1471-2164/8/430
Figure
This
paired
is tthe
6score
second set of genes that show an overall negative
This is the second set of genes that show an overall negative
paired t score. Here again we note that not all time points
display less than one expression value ratios. For example,
NOS2 shows a very large rise at the earlier time points.
Thus, our system may be increasing glucose intake to
make up for lack of oxygen, and it seems that our system
may be undergoing hypoxia [23,24] at least at earlier time
points. According to [23], "...the molecular mechanisms
by which muscle contraction/hypoxia increase glucose
uptake are less well defined, although they appear to be
independent of the PI3K pathway. Most intriguing is the
observation that the recently identified hormone adiponectin also stimulates skeletal muscle glucose uptake in
a PI3K-independent manner." The adiponectin gene is
not part of the hypoxia gene set. We went to our dataset to
see if seemed to show a trend similar to the genes in our
hypoxia pathway. The adiponectin gene showed an upregulation or a rise at earlier time points dropping to a downregulation at later time points (plot not shown).
To our knowledge this is the first evidence that tissue
stretch may lead to hypoxia. However, we should stress
that we do not have replicates at each time point. Thus,
the conclusions on the time trajectories follow from the
results of the enrichment analysis and not the other way
round. Because we feel that the hypoxia pathway is important, we then study its time trajectory in more detail for
further insights.
Conclusion
scorefirst5set of genes that show an overall negative paired t
The
Figure
The first set of genes that show an overall negative paired t
score. All these genes show a dip at the earlier time points in
the stretch to control expression ratios.
Our results show that the hypoxia pathway is clearly the
most important in this dataset. This finding gives us
insights not only into the biology that underlies the tissue
response to forces, but also into clinical interventions that
could be designed to mimic within wounded tissue the
effects of forces without all the negative effects that forces
themselves create (wound separation, pain and so on).
Mimicking the effects of forces through the excitation of
important parts of our hypoxia pathway through the use
of interference RNA or other gene therapy interventions
Page 6 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
could make an important impact on the suffering that
delayed wound healing creates in various disorders, primary among them being diabetes.
Methods
Applying forces to rat ears and obtaining the ear RNA
Male wistar rats weighing approximately 180 g were chosen for this study. All animal studies were conducted
according to institutional guidelines. Strips of latex were
fastened to the ears and, using appropriately calibrated
rubber bands, forces measuring 50 g were applied to the
ears at three points (Figure 2). Eight time points were sampled at 1 h, 4 h, 9 h, 13 h, 19 h, 48 h, 72 h, 96 h. Three rats
were used per time point. One ear of each rat (chosen randomly) was stretched while the other served as control. At
the time points listed above, the ears were excised and
immediately frozen in liquid nitrogen and then stored at
-80°C. The three stretched ears at each time point and the
three unstretched ears at the same point were separately
pooled (70 to 80% of the whole ear was used) and the
RNA extractions carried out. The RNA was then hybridized to RAE 230 2.0 Affymetrix rat gene chips. MAS5 was
used to obtain the expression values from the images.
Expression datasets will be deposited at geodatasets.
Analysis scheme
To gain insights into the molecular mechanisms and pathways underlying the cellular response to forces and tissue
stretch, a gene expression analysis was conducted by
stretching rat ears (see Figure 2) over a period of five days
and then obtaining their expression profiles using Affymetrix arrays. Traditional techniques that analyze gene
expression datasets rely on clustering techniques that do
not take advantage of pre-existing information about
pathways thus not taking account of biological context.
This is also true of other analyses that rank genes by fold
change or other measures of significance. Enrichment
techniques which include The Absolute Enrichment (AE),
The Gene Set Enrichment Analysis (GSEA), and what we
call The Down Regulated Analysis (DRE) are powerful
analysis techniques that not only take advantage of preexisting pathway knowledge but also in their implementation increase the signal to noise ratio thereby giving us a
higher chance of capturing subtle signals in the expression
set. Enrichment techniques start by using a ranking metric
to reorder the expression dataset.
Next, a priori created groups of genes are scored on this
reordered dataset and ranked from most highly scored at
the top of the reordered dataset to the least highly scored
or 'enriched' groups or gene sets. Thus, rather than looking at significance of each gene within the expression dataset, we look at significance of groups of genes. Since the
gene set enrichment analysis is not limited by the type of
statistic used, we should use a statistic that will be best at
http://www.biomedcentral.com/1471-2164/8/430
elucidating the changes between the groups that we are
studying. We thus conducted our analysis by reordering
our dataset using the paired t-statistic as our ranking metric (as our dataset consists of paired data points and thus
this statistic will be best for capturing differences). Enrichment techniques can be powerful tools in analyzing data.
However, within the enrichment analysis framework,
people have focused solely on upregulation. They have
perhaps neglected to look at down regulation. Downregulation itself can be just as important as upregulation [5].
However, both downregulated and upregulated (enrichment) analyses may miss differential effects that show
some balance between up and downregulation (what
essentially we have termed "homeostatic" systems) [5].
Let's look at a (metabolic) pathway (with feedback) to
make this more clear. Let's say a pathway has 3 members
A through C, and let's say A leads to upregulation of B
which leads to upregulation of C (Figure 7). Further, if B
can be upregulated independently of A, then B will downregulate A and if C can be upregulated independently of B,
then its upregulation will downregulate both A and B (Figure 8). And further, if C itself feeds into another pathway
which itself (the pathway) may be upregulated independently of C, then that pathway when independently upregulated will downregulate A, B, and C (Figure 9). We can
essentially perturb a pathway at any point from start to
finish (and not just at the start or at the end). If we perturb
it in between such that there is a good mixture of both
upregulation (coming from the downstream parts of the
pathway) and downregulation (coming from the
upstream parts of the pathway through feedback mechanisms), then this pathway may be captured by the absolute enrichment analysis (see Figure 10). For more details
on absolute enrichment see below as well as ref [5]. (It
must be noted that pathways don't have to have this serial
relationship as in our conceptualized pathway. There can
be bifurcations and so on.)
In essence, we are really looking for a change between an
"affected" condition and a "nonaffected" or control condition. A change can be either an upward change (upregulation) or a downward change (downregulation). Thus,
we're really looking for the highest differential regulation
[5]. Sometimes, the absolute enrichment technique captures gene sets that are seen (at the very top) in either the
up or down regulated analyses. Many times, however, it
captures other gene sets. Because we run an exhaustive
search through enrichment techniques that encompasses
both up (or down) regulated and absolute enrichment
techniques, we test whether the absolute enrichment technique captures anything extra at the top (of the list of
ranked gene sets) compared with either of the up or
downregulated techniques.
Page 7 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
Aigure
F
is downregulated
7
when B is upregulated independently of
A is downregulated when B is upregulated independently of
A.
When what the absolute enrichment captures is found at
the top of the up or downregulated analyses, then this
analysis doesn't add new information (although a few of
the rest of the top ranked gene sets in all the enrichment
analyses can still be useful to study). However, when it
does capture something new, we focus on it (or at least
look at its result) because what it has captured is the most
differentially expressed [5]. In our analysis, we captured a
new gene set that was higher ranked than even the highest
ranked upregulated and downregulated gene sets. Thus,
we studied this gene set in more detail. This included running a co-expression network analysis (the fact that we
could even run this analysis was driven by the small size of
the top ranked hypoxia gene set).
http://www.biomedcentral.com/1471-2164/8/430
it whole
The
stream
Figure
pathway
9 pathway
(pathway
can be2)downregulated
is upregulated when
independently
a down- of
The whole pathway can be downregulated when a downstream pathway (pathway 2) is upregulated independently of
it.
(since it captured the most differentially regulated gene
set). Our results show that the response to hypoxia pathway was the most differentially regulated pathway and
that this pathway showed a homeostatic (comprising
components of upregulation and downregulation)
response. We believe that this pathway is being most differentially expressed through a homeostatic perturbation.
The rationale for the AE is that in a time series analysis it
may be important to see how the system responds to the
(mechanical stretch) perturbations that we have imposed
on the system by regulating itself. Pathways are essentially
composed of elements that respond in tandem to perturbations. Often these responses are self controlled through
feedback mechanisms and thus homeostatic response
Thus, to recap, our analysis scheme consisted of choosing
the appropriate ranking metric (or statistic), running the
three enrichment techniques, and then focusing on the
gene set obtained from the absolute enrichment analysis
Figure
A
pendently
and B 8both
of Bare downregulated when C is upregulated indeA and B both are downregulated when C is upregulated independently of B.
The
vidualpathway
Figure
parts
10 ofisthe
shown
pathway
by the
arethick
not horizontal
depicted) black line (indiThe pathway is shown by the thick horizontal black line (individual parts of the pathway are not depicted). The upward
pointing black line with arrow shows an input that positively
regulates a component towards the middle of the pathway
upregulating the downstream parts of the pathway while
leading to downregulation of the upstream components (as
explained in Figs A through C above).
Page 8 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
analyses can often give us powerful indicators of pathways
that are important in gene expression datasets.
Enrichment analyses
Our data analysis uses enrichment techniques to understand important pathways in our system. Enrichment
techniques start out by reordering the dataset using an
appropriate statistic (please see Figure 11 for an overview
of these techniques using absolute enrichment as an
example). Consider the dataset to be a rectangular two
dimensional array with the different conditions (control
or stretch) represented by columns and the different genes
represented along the rows. A suitable statistic is chosen
that best helps us differentiate between stretch and control for each gene. Mootha's implementation of the gene
set enrichment analysis (GSEA) [25] used the signal to
noise ratio statistic to differentiate between the affected
condition in his dataset and the control condition. Our
dataset has paired samples (in the sense that one ear of the
same rat was subjected to stretch while the other ear from
the same rat at the same time point served as control).
Thus, a better statistic to use in our case is the paired t-statistic since it takes better account of pair-wise differences.
Enrichment techniques are not limited to the type of statistic used as the ranking metric. Any statistic that best lets
us differentiate between the two classes (control versus
stretch) can be used [5] (please see the Appendix for a
description of the paired t-statistic).
Once the ranking metric is calculated, the dataset is reordered (in our case, we used the paired t statistic). This is
where the three enrichment techniques that we used create different orderings. The GSEA gives us an ordering
from most upregulated to the least upregulated. The
downregulated enrichment or DRE ranks from most
downregulated to the least downregulated. The absolute
enrichment [5] uses the absolute value of the ranking metric (the paired t-statistic in our analysis) and then reorders
the dataset. Whereas both the standard GSEA [25] and the
DRE look for unidirectional regulations, the AE looks at a
combined regulation and therefore is more effective at
picking homeostatic systems. The absolute enrichment
(AE) technique [5] looks at homeostatic perturbations
rather than sole up or down regulations. Because it takes
absolute values of the genes and then enriches gene sets
on this absolute value ranked dataset, the AE becomes less
sensitive to less perturbed genes. Once the dataset is reordered, we then test various a priori created gene sets to see
how important each is vis-à-vis the reordering (construction of gene sets is explained in the next sub section). In
contrast to strategies that look purely for most important
genes, we are now looking for significant groups of genes
or gene sets. When we look at a single gene, we look for
how far up on a reordered dataset (reordered say by fold
change) the gene finds itself. In an enrichment technique,
http://www.biomedcentral.com/1471-2164/8/430
however, we look for how far up the gene set finds itself in
the reordered dataset. The measure of how far 'up' a gene
set is placed on the reordered dataset is given by the
enrichment score (ES). The enrichment score is calculated
in the following way. Calculate the Kolmogorov Smirnov
statistic for each probeset ID in our dataset given by,
XN = −
G
N −G
if the probeset ID is not part of the gene set and by,
X=−
N −G
G
if the probeset ID is part of the gene set, where G is the
number of genes in the gene set and N is the total number
of genes (or rather probeset IDs in the whole dataset – we
had 31099 probeset IDs).
We next compute a running sum of this statistic on the
reordered dataset. Where the running sum reaches a maximum score is our enrichment score for the gene set in
question [25,5]. (Figure 11 shows the overall steps in running enrichment techniques). The gene sets are then
ranked from highest enrichment score to the lowest. To
test whether the results of the analysis are significant, we
perform permutation testing of the dataset. To obtain the
permutes, we swapped random stretch samples with random control samples. One thousand permuted datasets
were constructed. All three enrichment analyses were run
on each of these newly constructed datasets, giving us one
thousand rankings of gene sets based on their enrichment
scores (for each enrichment analysis). We then counted
the number of times each gene set achieved top rank in
the one thousand permutes. This number divided by 1000
gave us the permutation test P value. If this number was
lower than 0.05, then the gene set was significant. For
more details on the permutation test, please see Mootha
et al.[25]. We didn't run technical replicates at each time
point because our analysis is looking at an aggregate
change between the stretched ear and control ear conditions across all time points. By doing this we look for overall changes between the eight time points versus their
corresponding paired controls. We next describe how the
various gene sets that were used in the enrichment techniques were constructed.
Construction of gene sets
Many gene sets were compiled by the use of rat orthologs
of previously published genesets [25] (according to [26]
"it is estimated that 90% of rat genes have orthologs in the
mouse and human genomes that have persisted since they
shared a common ancestor"). Gene sets are essentially colPage 9 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
http://www.biomedcentral.com/1471-2164/8/430
Figure
Steps
in 11
conducting enrichment analyses using absolute enrichment as an example
Steps in conducting enrichment analyses using absolute enrichment as an example.
Page 10 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
lections of genes. To derive additional gene sets that may
show relevance, we searched the literature for different
analyses that have been performed on cell excitation such
as response to serum. When these analyses listed groups
of genes obtained through various clustering or classifying
techniques, we created gene sets for the various clusters
listed in the papers. Other gene sets were created by looking up all possible relevant keywords at the geneontology.org website (angiogenesis, response to mechanical
stimulus and so on). The gene sets obtained through the
various mechanisms outlined above when they comprised genes not in Affymetrix Probeset ID format were
then converted to collections of Probeset IDs through various platforms (The Affymetrix website, matchminer,
Onto tools from the Intelligent systems Bioinformatics
Laboratory). Some of the gene sets that were obtained
were derived from clusters obtained through classifying
gene expression datasets. Sometimes, the clusters were
annotated by the publications as being enriched for certain pathways or GO categories. When they were not (as
for example the 'c' clusters in Mootha's published gene set
list [25]), we used EASE (a tool for obtaining enrichment
information on groups of genes) as we explain next. A
total of 173 gene sets were constructed.
EASE
For calculation of GO category enrichment, we used EASE.
EASE is a program that determines the over-represented
GO categories in a group of genes (specified either by
probeset ID, accession number or other identifier) by calculating the hypergeometric ratio of the GO annotations
of the genes found in an analysis versus the background
distribution of each of the GO annotations. It should be
noted that EASE is distinct from the various enrichment
tests that are used in this paper. Enrichment techniques
(such as AE, GSEA up or down regulated enrichment)
measure differential expression of a set or group of genes.
These groups of genes are pre-defined by us independently of the data set. EASE is independent of the enrichment techniques that we use. We can run EASE on any
(possibly random) group of genes. EASE will then find
what annotations are over-represented in these groups.
Enrichment techniques try to see which pre-defined gene
sets are most important in a re-ordered data set (thus the
gene sets are previously defined and then compared to the
data set), while EASE takes any group of genes and tries to
find which annotations are over-represented in that
group. Through the use of the various enrichment techniques, we found the hypoxia gene set to be the most significantly differentially expressed. We next ran a coexpression network analysis on this gene set.
Co-expression network analysis
To understand connection hubs in the hypoxia pathway,
a co-expression network was constructed similar to the
http://www.biomedcentral.com/1471-2164/8/430
methodology presented by Zhang and Horvath [27]. The
stretch to control expression value ratios were obtained at
each time point. Correlations were then obtained for all
possible pairs of genes across all the time point ratios.
Next we took a threshold of 0.5 of the absolute values of
the correlations leaving us with correlations between
genes that were higher than 0.5. The genes were then 'connected' to other genes when their pairwise correlations
were above this threshold.
Appendix
Paired t-statistic
The paired t-statistic is given by,
t = (Y − X )
∑
n
n(n −1)
(Yi − X i ) 2
i =1
where,
X i = ( X i − X),
Yi = (Yi − Y ),
∑
X=
n
i =1
Xi
n
∑
Y =
n
i =1
,
Yi
n
The 'Y' variable here stands for the stretch condition while
the 'X' variable stands for the control condition. We calculate the paired t-statistic for each gene ('n' is eight, the
number of time points).
Authors' contributions
VS performed the data analysis and the animal experiments. He also wrote the paper. DO provided the animals
and lab facilities, insights and suggestions. IK provided
the gene chips, insights and suggestions. All authors read
and approved the final manuscript.
Acknowledgements
The authors would like to thank Giorgio Pietramaggiori, Perry Liu, and
Robert Golden for help with the animal experiments.
References
1.
2.
Venturi ML, Attinger CE, Mesbahi AN, Hess CL, Graw KS: Mechanisms and clinical applications of the vacuum-assisted closure (VAC) Device: a review. Am J Clin Dermatol 2005, 6:185-194.
Saxena V, Hwang CW, Huang S, Eichbaum Q, Ingber D, Orgill DP:
Vacuum-assisted closure: microdeformations of wounds and
cell proliferation. Plast Reconstr Surg 2004, 114:1086-96; discussion
1097-8.
Page 11 of 12
(page number not for citation purposes)
BMC Genomics 2007, 8:430
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
Huang S, Ingber DE: The structural and mechanical complexity
of cell-growth control. Nat Cell Biol 1999, 1:E131-8.
Mahomed N, McKee N, Solomon P, Lahoda L, Gross AE: Soft-tissue
expansion before total knee arthroplasty in arthrodesed
joints. A report of two cases. J Bone Joint Surg Br 1994,
76(1):88-90.
Saxena V, Orgill D, Kohane I: Absolute enrichment: gene set
enrichment analysis for homeostatic systems. Nucleic Acids Res
2006, 34:e151.
Maiti P, Singh SB, Sharma AK, Muthuraju S, Banerjee PK, Ilavazhagan
G: Hypobaric hypoxia induces oxidative stress in rat brain.
Neurochem Int 2006, 49:709-716.
Sikorski EM, Hock T, Hill-Kapturczak N, Agarwal A: The story so
far: Molecular regulation of the heme oxygenase-1 gene in
renal injury. Am J Physiol Renal Physiol 2004, 286:F425-41.
Liang M, Croatt AJ, Nath KA: Mechanisms underlying induction
of heme oxygenase-1 by nitric oxide in renal tubular epithelial cells. Am J Physiol Renal Physiol 2000, 279:F728-35.
Keyse SM, Tyrrell RM: Heme oxygenase is the major 32-kDa
stress protein induced in human skin fibroblasts by UVA
radiation, hydrogen peroxide, and sodium arsenite. Proc Natl
Acad Sci U S A 1989, 86:99-103.
Howard AB, Alexander RW, Nerem RM, Griendling KK, Taylor WR:
Cyclic strain induces an oxidative stress in endothelial cells.
Am J Physiol 1997, 272:C421-7.
Alam J, Cook JL: Transcriptional regulation of the heme oxygenase-1 gene via the stress response element pathway. Curr
Pharm Des 2003, 9:2499-2511.
Hayashi S, Takamiya R, Yamaguchi T, Matsumoto K, Tojo SJ, Tamatani
T, Kitajima M, Makino N, Ishimura Y, Suematsu M: Induction of
heme oxygenase-1 suppresses venular leukocyte adhesion
elicited by oxidative stress: role of bilirubin generated by the
enzyme. Circ Res 1999, 85:663-671.
Lee PJ, Alam J, Wiegand GW, Choi AM: Overexpression of heme
oxygenase-1 in human pulmonary epithelial cells results in
cell growth arrest and increased resistance to hyperoxia.
Proc Natl Acad Sci U S A 1996, 93:10393-10398.
Lee TS, Chang CC, Zhu Y, Shyy JY: Simvastatin induces heme
oxygenase-1: a novel mechanism of vessel protection. Circulation 2004, 110:1296-1302.
Morse D, Choi AM: Heme oxygenase-1: the "emerging molecule" has arrived. Am J Respir Cell Mol Biol 2002, 27:8-16.
Coito AJ, Buelow R, Shen XD, Amersi F, Moore C, Volk HD, Busuttil
RW, Kupiec-Weglinski JW: Heme oxygenase-1 gene transfer
inhibits inducible nitric oxide synthase expression and protects genetically fat Zucker rat livers from ischemia-reperfusion injury. Transplantation 2002, 74:96-102.
Imai T, Morita T, Shindo T, Nagai R, Yazaki Y, Kurihara H, Suematsu
M, Katayama S: Vascular smooth muscle cell-directed overexpression of heme oxygenase-1 elevates blood pressure
through attenuation of nitric oxide-induced vasodilation in
mice. Circ Res 2001, 89:55-62.
Turcanu V, Dhouib M, Poindron P: Heme oxygenase inhibits
nitric oxide synthase by degrading heme: a negative feedback regulation mechanism for nitric oxide production.
Transplant Proc 1998, 30:4184-4185.
Duckers HJ, Boehm M, True AL, Yet SF, San H, Park JL, Clinton Webb
R, Lee ME, Nabel GJ, Nabel EG: Heme oxygenase-1 protects
against vascular constriction and proliferation. Nat Med 2001,
7:693-698.
Loike JD, Cao L, Brett J, Ogawa S, Silverstein SC, Stern D: Hypoxia
induces glucose transporter expression in endothelial cells.
Am J Physiol 1992, 263:C326-33.
Heikkinen S: Glucose metabolism in heterozygous hexokinase
II knock-out mice. In Medicine Kuopio, University of Kuopio;
2004:83.
Uldry M, Thorens B: The SLC2 family of facilitated hexose and
polyol transporters. Pflugers Arch 2004, 447:480-489.
Wijesekara N, Thong FSL, Antonescu CN, Klip A: Diverse signals
regulate glucose uptake into skeletal muscle. Canadian Journal
of Diabetes 2006, 30:80-88.
Cartee GD, Douen AG, Ramlal T, Klip A, Holloszy JO: Stimulation
of glucose transport in skeletal muscle by hypoxia. J Appl Physiol 1991, 70:1593-1600.
Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar
J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, Houstis N, Daly
http://www.biomedcentral.com/1471-2164/8/430
26.
27.
MJ, Patterson N, Mesirov JP, Golub TR, Tamayo P, Spiegelman B,
Lander ES, Hirschhorn JN, Altshuler D, Groop LC: PGC-1alpharesponsive genes involved in oxidative phosphorylation are
coordinately downregulated in human diabetes. Nat Genet
2003, 34:267-273.
Mullins LJ, Mullins JJ: Insights from the rat genome sequence.
Genome Biol 2004, 5:221.
Zhang B, Horvath S: A general framework for weighted gene
co-expression network analysis. Stat Appl Genet Mol Biol 2005,
4:Article17.
Publish with Bio Med Central and every
scientist can read your work free of charge
"BioMed Central will be the most significant development for
disseminating the results of biomedical researc h in our lifetime."
Sir Paul Nurse, Cancer Research UK
Your research papers will be:
available free of charge to the entire biomedical community
peer reviewed and published immediately upon acceptance
cited in PubMed and archived on PubMed Central
yours — you keep the copyright
BioMedcentral
Submit your manuscript here:
http://www.biomedcentral.com/info/publishing_adv.asp
Page 12 of 12
(page number not for citation purposes)